1. Field of the Invention
The present invention generally relates to methods and systems for generating an inspection process for a semiconductor wafer or reticle inspection system. Certain embodiments relate to a computer-implemented method that includes selecting values of one or more image acquisition, sensitivity and nuisance removal parameters, which are determined to produce the best and most complete inspection data, to be used in the inspection process.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Inspection processes are used at various times during a semiconductor manufacturing process to detect defects on a specimen such as a reticle and a wafer. Inspection processes have always been an important part of fabricating semiconductor devices such as integrated circuits. However, as the dimensions of semiconductor devices decrease, inspection processes become even more important to the successful manufacture of acceptable semiconductor devices. For instance, as the dimensions of semiconductor devices decrease, detection of defects of decreasing size has become necessary since even relatively small defects may cause unwanted aberrations in the semiconductor devices. Accordingly, much work in the inspection field has been devoted to designing inspection systems that can detect defects having sizes that were previously negligible.
Inspection for many different types of defects has also become more important recently. For instance, in order to use the inspection results to monitor and correct semiconductor fabrication processes, it is often necessary to know what types of defects are present on a specimen. In addition, since controlling every process involved in semiconductor manufacturing is desirable to attain the highest yield possible, it is desirable to have the capability to detect the different types of defects that may result from many different semiconductor processes. The different types of defects that are to be detected may vary dramatically in their characteristics. For example, defects that may be desirable to detect during a semiconductor manufacturing process may include thickness variations, particulate defects, scratches, pattern defects such as missing pattern features or incorrectly sized pattern features, and many others having such disparate characteristics.
Many different types of inspection systems have been developed to detect the different types of defects described above. In addition, most inspection systems are configured to detect multiple different types of defects. In some instances, a system that is configured to detect different types of defects may have adjustable image acquisition and sensitivity parameters such that different parameters can be used to detect different defects or avoid sources of unwanted (nuisance) events. For instance, the spot or pixel size, polarization or the algorithm settings for the angles of collection may be different for an inspection process used to detect particulate defects than for an inspection process used to detect scratches.
Although an inspection system that has adjustable image acquisition and sensitivity parameters presents significant advantages to a semiconductor device manufacturer, these inspection systems are useless if the incorrect image acquisition and sensitivity parameters are used for an inspection process. For example, incorrect or non-optimized image acquisition and sensitivity parameters may produce such high levels of noise that no defects can be detected in the generated inspection data. In addition, since the defects, process conditions and noise on a specimen such as a reticle and a wafer may vary dramatically (and since the characteristics of the specimen itself may vary dramatically), the best image acquisition and sensitivity parameters for detecting the defects on a particular specimen may be difficult, if not impossible, to predict. Therefore, although using the correct image acquisition and sensitivity parameters will have a dramatic effect on the results of inspection, it is conceivable that many inspection processes are currently being performed with incorrect or non-optimized image acquisition and sensitivity parameters.
The task of setting up an inspection process for a particular specimen and a particular defect of interest may be extremely difficult for a user particularly when an inspection system has a relatively large number of adjustable image acquisition settings and sensitivity parameters. In addition, it may be impossible to know whether the best inspection process has been found unless all possible combinations of the image acquisition parameters have been tested. However, most inspection processes are currently set up using a large number of manual processes (e.g., manually setting the image acquisition parameters, manually analyzing the resulting inspection data, etc.). As such, setting up the inspection process may take a relatively long time. Furthermore, depending on the types of specimens that will be inspected with the inspection system, a different inspection process may need to be set up for each different type of specimen. Obviously, therefore, setting up the inspection processes for all of the different specimens that are to be inspected may take a prohibitively long time.
Even with the correct image acquisition settings, algorithms for separating the defects from the noise and nuisance events need to be tuned for optimal inspection performance.
In some cases, the user may not know the operating range of a sensitivity parameter, which can lead to beginning the setup process with one or more sensitivity parameter settings in a state which will lead to excessive numbers of defects, or one that will not be sufficiently sensitive.
Accordingly, it may be advantageous to develop methods and systems for generating an inspection process for an inspection system that reduce the burden of setting up the inspection process on the user while increasing the optimization of the parameters of the inspection process and decreasing the time involved in generating the inspection process.